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Contribution à l'optimisation globale : approche déterministe et stochastique et application

Abstract : This thesis concerns the global optimization of a non convex function under non linear restrictions, this problem cannot be solved using the classic deterministic methods like the projected gradient algorithm and the sqp method because they can solve only the convex problems. The stochastic algorithms like the genetic algorithm and the simulated annealing algorithm are also inefficients for solving this type of problems. For solving this kind of problems, we try to perturb stocasicly the deterministic classic method and to combine this perturbation with genetic algorithm and the simulated annealing. So we do the combination between the perturbed projected gradient and the genetic algorithm, the perturbed sqp method and the genetic algorithm, the perturbed projected gradient and the simulated annealing, the Piyavskii algorithm and the genetic algorithm. We applicate the coupled algorithms to different classic examples for concretited the thesis. For illustration in the real life, we applicate the coupled perturbed projected gradient end the genetic algorithm to logistic problem eventuelly transport. In this view, we sold the efficient practices.
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Mohamed Zeriab Es-Sadek. Contribution à l'optimisation globale : approche déterministe et stochastique et application. Mathématiques générales [math.GM]. INSA de Rouen; Université Mohammed V - Agdal, 2009. Français. ⟨NNT : 2009ISAM0010⟩. ⟨tel-00560887⟩

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